The Plan¶
- Some background on the Polymath Projects and their place in the literature.
- The goal of this talk.
- Online research: methodology and general insights.
- Formal models of scientific communities.
- The role of higher-order information for collaboration.
- Extracting patterns of interaction from the data.
- Roles in the collaboration.
- Conclusion: Central figures as available active aggregators.
The Polymath Projects¶
I’m interested in the question of whether it is possible for lots of people to solve one single problem rather than lots of people to solve one problem each¶
- Open collaborative mathematics project initiated by Timothy Gowers in Februari 2009.
- Polymath 1 was devoted to the task of finding an elementary proof of the Density Hales-Jewett Theorem.
- Terence Tao soon joined Gowers as one of the leading figures.
- Probable success was announced less than 6 weeks after the start of the project.
- Results were published under the name "D.H.J. Polymath".
- Still active: Polymath 10 and 11 started in the last 6 months.
Disciplinary perspectives¶
- Philosophy of mathematical practices: Real mathematics in action.
- Social studies of the internet: e-research and citizen science.
- Social epistemology: structure of scientific communities.
Existing literature¶
- By insiders Gowers & Nielsen. 2009. Massively collaborative mathematics. Nature 46. Michael Nielsen. "Reinventing discovery : the new era of networked science".
- Popular science New Scientist: “Mathematics becomes more sociable”, “How to build the global mathematics brain”.
- Quantitative and social network analysis of Polymath 1 by Cranshaw & Kittur (2011).
- Studied within the "Social Machine of Mathematics" project by Pease and Martin.
- More publications by: Varshney (2012), Stefaneas & Vandoulakis (2012).
- Van Bendegem, J.P., 2014, Logic, Methodology and Philosophy of Science. Proceedings of the 14th International Congress (Nancy). Logic and Science Facing the New Technologies, Mathematics and the new technologies, part III: The cloud and the web of proofs. College Publications, London, pp. 427-39.
- Allo, P., Bendegem, J.P.V. & Kerkhove, B.V. 2013, Mathematical Arguments and Distributed Knowledge, in Aberdein & Dove (eds), The Argument of Mathematics, Springer Netherlands, Dordrecht, pp. 339-60.
What I want to do¶
- Go beyond the retelling of the story of Polymath.
- Evaluate whether this is really a new kind of mathematics.
- Focus on division of epistemic labour, information-flow, community-structures and diversity of roles.
- Use this as a case-study in the study of the structure of scientific communities.
What I will leave aside¶
- The role of ICT in mathematical research.
- Polymath as e-research or as citizen science.
- Quantitative insights.
- Substantial network-analysis (e.g. compute centrality-measures and triadic closure, look for connected components, ...).
- The messy details of cleaning the data, and some of the obstacles I faced.
Methodological aims¶
- Rely on how formal methods can be used to study scientific communities, interaction, and information-flow.
- Not as a-priori methods, but as a source of concepts to guide an empirical study.
What I did¶
From a nested discussion to a tree of comments¶
A toy-example¶
- Blog-post (L0-comment)
- L1-comment: timestamp: ..., author: Alice, content: ..., ...
- L1-comment: timestamp: ..., author: Bob, content: ..., ...
- L2-comment: timestamp: ..., author: Alice, content: ..., ...
- L2-comment: timestamp: ..., author: Carol, content: ..., ...
- L1-comment: timestamp: ..., author: Bob, content: ..., ...
- L2-sub-comment: timestamp: ..., author: Alice, content: ..., ...
- L3-comment: timestamp: ..., author: Bob, content: ..., ...
- L2-sub-comment: timestamp: ..., author: Alice, content: ..., ...
In [8]:
example_graph = nx.DiGraph()
example_graph.add_nodes_from([1,2,3,4,5,6,7])
example_graph.add_edges_from([(3,2), (4,2), (6,5), (7,6)])
matplotlib.style.use(SBSTYLE)
nx.draw_networkx(example_graph, pos={1: (0,0), 2: (0,1), 3: (1,1), 4: (1, 1.5), 5: (0, 2), 6: (1, 2), 7: (2,2)},
node_list=[1,2,3,4,5,6,7],
node_color=[1, 2, 1, 3, 2, 1,2])
limits=plt.axis("off")
- Blog-post is not included.
- Edges from child-node to parent-node.
- Level of comment = distance to blog-post.
- A project is a list of threads.
To an interaction-network¶
In [9]:
example_graph2 = nx.DiGraph()
example_graph2.add_nodes_from(['Alice', 'Bob', 'Carol'])
example_graph2.add_weighted_edges_from([('Alice', 'Bob', 2), ('Bob', 'Alice', 1), ('Carol', 'Bob', 1)])
matplotlib.style.use(SBSTYLE)
nx.draw_networkx(example_graph2, node_list=['Alice', 'Bob', 'Carol'], node_color=[1,3,2], node_size=1000)
limits=plt.axis("off")
And associated meta-data for each author¶
- Total number of comments
- Number of comments by comment-level.
- Total word-count.
- Timestamps of all comments.
A General Overview of Polymath¶
- Polymath1: New proofs and bounds for the density Hales-Jewett theorem. Initiated Feb 1, 2009; research results have now been published.
- Polymath2: Must an “explicitly defined” Banach space contain $c_0$ or $l_p$? Initiated Feb 17, 2009; attempts to relaunch via wiki, June 9 2010.
- Polymath3: The polynomial Hirsch conjecture. Proposed July 17, 2009; launched, September 30, 2010.
- Polymath4: A deterministic way to find primes. Proposed July 27, 2009; launched Aug 9, 2009. Research results have now been published.
- Polymath5: The Erdős discrepancy problem. Proposed Jan 10, 2010; launched Jan 19, 2010. Activity ceased by the end of 2012, but results from the project were used to solve the problem in 2015.
- Polymath6: Improving the bounds for Roth's theorem. Proposed Feb 5, 2011. Partial result published by non-participant
- Polymath7: Establishing the Hot Spots conjecture for acute-angled triangles. Proposed, May 31st, 2012; launched, Jun 8, 2012.
- Polymath8: Improving the bounds for small gaps between primes. Proposed, June 4, 2013; launched, June 4, 2013. Research results have now been published.
- Polymath9: exploring Borel determinacy-based methods for giving complexity bounds. Proposed, Oct 24, 2013; launched, Nov 3, 2013. “success of a kind”.
- Polymath10: improving the bounds for the Erdos-Rado sunflower lemma. Launched, Nov 2, 2015. ongoing
- Polymath11: proving Frankl's union-closed conjecture. Proposed Jan 21, 2016; launched Jan 29, 2016. ongoing
Sizes of the projects¶
In [10]:
from matplotlib.ticker import FuncFormatter
PROJECTS_TO_C = ["Polymath {}".format(i) for i in range(1, 12)]
PARTICIPANTS = Series([PM_FRAME.loc[project]['authors (accumulated)'].iloc[-1] for
project in PROJECTS_TO_C], index=PROJECTS_TO_C)
R_NETWORKS = Series([PM_FRAME.loc[project]['r_network'].dropna().iloc[-1] for project in PROJECTS_TO_C],
index=PROJECTS_TO_C)
WITH_D = [project for project in PROJECTS_TO_C if not PM_FRAME.loc[project]['research'].all()]
D_NETWORKS = Series([PM_FRAME.loc[project]['d_network'].dropna().iloc[-1] for project in WITH_D],
index=WITH_D)
R_PARTICIPANTS = R_NETWORKS.apply(lambda network: set(network.author_frame.index))
D_PARTICIPANTS = D_NETWORKS.apply(lambda network: set(network.author_frame.index))
COMMENTS = Series([PM_FRAME.loc[project]['number of comments (accumulated)'].iloc[-1] for
project in PROJECTS_TO_C], index=PROJECTS_TO_C)
df = DataFrame({'all threads': PARTICIPANTS, 'research threads': R_PARTICIPANTS, 'discussion threads': D_PARTICIPANTS},
index=PROJECTS_TO_C)
df['authors only active in research threads'] = df['research threads'] - df['discussion threads']
df['authors only active in "discussion" threads'] = df['discussion threads'] - df['research threads']
df['authors active in both types of threads'] = df['all threads'] - df['authors only active in research threads'] - df['authors only active in "discussion" threads']
for project in PROJECTS_TO_C:
if pd.isnull(df.loc[project]['authors only active in research threads']):
df.loc[project]['authors only active in research threads'] = df.loc[project]['all threads']
data = df[['authors only active in research threads', 'authors only active in "discussion" threads', 'authors active in both types of threads']]
data = data.applymap(lambda set: len(set) if pd.notnull(set) else 0)
matplotlib.style.use(SBSTYLE)
axes = data.plot(kind='bar', stacked=True, color=['steelblue', 'lightsteelblue', 'lightgrey'],
title="Number of participants per thread-type in each Polymath project\n Number of comments per project")
axes.set_ylabel("Number of participants")
axes.annotate('published', xy=(0, 115), xytext=(0, 130),
arrowprops=dict(facecolor='steelblue', shrink=0.05),
)
axes.annotate('published', xy=(3, 60), xytext=(1.5, 80),
arrowprops=dict(facecolor='steelblue', shrink=0.05),
)
axes.annotate('re-used', xy=(4, 130), xytext=(4.5, 140),
arrowprops=dict(facecolor='lightsteelblue', shrink=0.05),
)
axes.annotate('published', xy=(7, 155), xytext=(7.5, 170),
arrowprops=dict(facecolor='steelblue', shrink=0.05),
)
data2 = np.sqrt(COMMENTS)
axes2 = axes.twinx()
axes2.yaxis.set_major_formatter(FuncFormatter(lambda x, pos:"{:0.0f}".format(np.square(x))))
axes2.set_ylabel("Number of comments")
axes2.plot(axes.get_xticks(), data2.values,
linestyle='-', marker='.', linewidth=.5,
color='darkgrey')
Out[10]:
Size and evolution of the community¶
In [13]:
plot_community_evolution("Polymaths")
Importance of “early adopters”¶
In [15]:
select_n = plot_participation_evolution("Polymath", n=2)
The global Polymath community structure¶
(threshold: participation to at least two projects)
In [16]:
from mpl_toolkits.axes_grid1 import make_axes_locatable
authors_n = sorted([author for author, bool in select_n.items() if bool])
def general_heatmap(authors=None, binary=False, thread_level=True,
binary_method='average', method='ward', log=True,
fontsize=8):
if thread_level:
authors_filtered = list(ALL_AUTHORS)
try:
authors_filtered.remove("Anonymous")
except:
pass
data=PM_FRAME['comment_counter']
else:
authors_filtered = list(ALL_AUTHORS) if not authors else authors
try:
authors_filtered.remove("Anonymous")
except:
pass
data = get_last(POLYMATHS)[0]['comment_counter (accumulated)']
if binary:
as_matrix=np.array([[True if author in data[thread] else False for author in authors_filtered]
for thread in data.index])
Z_author = linkage(as_matrix.T, method=binary_method, metric='hamming')
Z_thread = linkage(as_matrix, method=binary_method, metric='hamming')
c, _ = cophenet(Z_author, pdist(as_matrix.T))
print("Cophenetic Correlation Coefficient with {}: {}".format(binary_method, c))
else:
as_matrix = []
for thread in data.index:
new_row = [data.loc[thread][author] for author in authors_filtered]
as_matrix.append(new_row)
as_matrix = np.array(as_matrix)
Z_author = linkage(as_matrix.T, method=method, metric='euclidean')
Z_thread = linkage(as_matrix, method=method, metric='euclidean')
c, _ = cophenet(Z_author, pdist(as_matrix.T))
print("Cophenetic Correlation Coefficient with {}: {}".format(method, c))
# start setting up plots
matplotlib.style.use(SBSTYLE)
fig, ax_heatmap = plt.subplots()
# compute and plot dendogram (top-plot)
ddata_author = dendrogram(Z_author, color_threshold=.07,
no_plot=True)
ddata_thread = dendrogram(Z_thread, color_threshold=.07, no_plot=True)
df = DataFrame(as_matrix, columns=authors_filtered)
cols = [authors_filtered[i] for i in ddata_author['leaves']]
df = df[cols]
rows = [df.index[i] for i in ddata_thread['leaves']]
df = df.reindex(rows)
# plot heatmap (bottom)
heatmap = ax_heatmap.pcolor(df,
edgecolors='w',
cmap=mpl.cm.binary if binary else mpl.cm.GnBu,
norm=mpl.colors.LogNorm() if log else None)
ax_heatmap.autoscale(tight=True) # get rid of whitespace in margins of heatmap
ax_heatmap.set_aspect('equal') # ensure heatmap cells are square
ax_heatmap.xaxis.set_ticks_position('bottom') # put column labels at the bottom
ax_heatmap.tick_params(bottom='off', top='off', left='off', right='off') # turn off ticks
ax_heatmap.set_title("Project-Engagement in Polymath")
ax_heatmap.set_yticks(np.arange(0.5, len(df.index)+.5, 1))
ax_heatmap.set_yticklabels(df.index + 1, fontsize=fontsize)
ax_heatmap.set_xticks(np.arange(len(df.columns)) + 0.5)
ax_heatmap.set_xticklabels(df.columns, rotation=90, fontsize=fontsize)
if not binary:
divider_h = make_axes_locatable(ax_heatmap)
cax = divider_h.append_axes("right", "3%", pad="1%")
plt.colorbar(heatmap, cax=cax)
lines = (ax_heatmap.xaxis.get_ticklines() +
ax_heatmap.yaxis.get_ticklines())
plt.setp(lines, visible=False)
plt.tight_layout()
general_heatmap(authors=authors_n, thread_level=False,
binary=False, log=True)
A natural grouping based on similarity¶
- Leaders (2): Tao & Gowers
- Core-participants (11): Peake -> Sauvaget
- Periphery (49): Kowalski -> mixedmath
- Outsider (1): Edgington (very active in PM5 and PM11, but not an early adopter)
The strange case of Polymath 8¶
- Most comments and most participants.
- Little commenting activity in the heatmap.
- Only 29 of the 180 participants to Polymath 8 overlap with other projects.
- Typical computational project: “a project to improve the bound $H=H_1$ on the least gap between consecutive primes that was attained infinitely often”
The economic model¶
What is the optimal structure of communication (given certain assumptions) for a given epistemic aim?¶
- Bala & Goyal model adapted by Zollman.
- Communities are represented as graphs.
- Individual agents are Bayesian learners who observe other agents and try to improve on the basis of their observations.
- Success = converging to the truth.
- Results based on simulations (typically in netlog).
- Widely cited insight: maximal connectivity is not the optimal organisation (being shielded from potentially bad information is beneficial).
- Recently criticized by Rosenstock, O'Connor and Bruner: results not robust when parameters are modified. E.g. for >6 communities.
- A-priori approach with no connection to actual scientific communities (cfr. Martini & Pinto on need to connect simulations with the data).
Further assumptions¶
- Only factual information is exchanged.
- Agent-behaviour is reduced to choosing an option.
- Graph-based typology of communities
- Only one-to-one exchanges.
Not readily applicable to Polymath¶
- Does not fit the open-ended character of Polymath.
- Interactions in Polymath cannot be reduced to observations.
- Collaboration cannot be reduced to information-exchange between individual agents and individual success.
Further challenge¶
- Public character of Polymath suggests maximal accessibility.
Teamwork and higher-order information¶
Active collaboration relies on strong group-attitudes¶
- Mutual intentions.
- Common intentions.
- Group commitments.
- Higher-order attitudes, and common belief in particular are essential for teamwork (Dunin-Keplicz & Verbrugge 2010).
- But common information, belief and knowledge require public announcements and hence a common informational context.
- Public announcements exclude all ignorance regarding what was announced, and regarding the effect of the announcement itself.
- Impossible to achive through asynchronous communication.
Announcement-types as a typology for collaborative networks¶
- Access to information does not explain why teamwork succeeds.
- Reliable information-aggregation isn't sufficient for teamwork.
- Patterns of one-to-many communication as a finer typology.
Note: Zollman's model could be seen as private announcements/observations only.
Three perspectives on information-flow¶
High-level co-presence¶
- Criterion: participation in the same thread
- Account: who might have interacted
In [19]:
project_heatmap("Polymath 4", cluster_threads=True, method='average', log=True, fontsize=10)
In [20]:
project_heatmap("Polymath 1", cluster_threads=False, method='average', log=True, fontsize=9)
Direct Interaction¶
- Criterion: direct replies
- Account: who did interact
In [22]:
draw_network("Polymath 4", graph_type="interaction", reset=True)
- Weighted directed graph.
Finely grained co-presence¶
- Criterion: high probability of mutual awareness
- Account: who probably interacted
- But how can this information be extracted from actual interactions?
Proposal 1: Who's in the centre of discussion?¶
The “centre of discussion” metaphor¶
- Contributing puts one in the centre of discussion.
- Closeness to the centre of discussion is an indication for availability.
- Being close to the centre of discussion is sufficient to know who's also close to the centre of discussion.
- The above is common knowledge.
- Modulo vagueness (and abstracting from the temporal ordering) about what it means to be close to the centre of discussion, the set A of agents that are close to the centre of discussion is common knowledge within A.
- This approximates the conditions for the members of A to make public announcements to the members of A.
- Common knowledge within A of what is announced is highly probable.
Modelling assumption¶
- Distance from the centre of discussion is proportional to time elapsed since last comment.
In [23]:
import io
import base64
from IPython.display import HTML
video = io.open('FIGS/out.m4v', 'r+b').read()
encoded = base64.b64encode(video)
HTML(data='''<video alt="test" controls>
<source src="data:video/mp4;base64,{0}" type="video/mp4" />
</video>'''.format(encoded.decode('ascii')))
Out[23]:
Proposal 2: Identify coherent episodes in discussion-threads¶
Clustering of time-lines¶
- Used
MeanShiftalgorithm to identify clusters in lists of time-stamps. - Motivation: bursts of activity as proxy for episodes in a discussion-thread.
In [25]:
plot_activity("Polymath 4", color_by="author", first="2009-07-01", last="2009-08-15")
Co-location network¶
In [26]:
draw_network("Polymath 4", graph_type="cluster")
- Weighted undirected graph.
- Edges indicate co-presence in an episode.
- Weight of edges (intensity) indicates repeated co-presence.
In [27]:
draw_network("Polymath 4", graph_type="interaction")
In [29]:
print("Density of co-location network: ", nx.density(pm4_c))
print("Density of interaction network: ", nx.density(pm4_i))
Evaluation¶
Proposal 1: Centre of Discussion¶
- Valuable metaphor.
- Inherent vagueness: how far does the centre of discussion reach?
- Not flexible: size of the centre of discussion can change over time.
- Compatible with one-to-many communications.
Proposal 2: Co-presence in episodes¶
- Precise bounds: episodes are exclusive and exhaustive.
- Flexible: length of episodes depends on commenting-activity.
- Unknowable: episodes can only be identified after the fact.
- Co-location network based on co-presence in an episode privileges binary relations between agents.
The role of leading figures¶
- Gowers and Tao are highly connected on all accounts we considered.
- They are almost constantly present in the centre of discussion.
- Blog-posts can be treated as public announcements.
- All participants can make public announcements to the group in the centre of discussion (or active in the current episode).
- Because presence in the centre of discussion is commonly known, the availability of leading figures is commonly known as well.
- Trust in central figures and common knowledge of their availability ensures successful aggregation, understood as further pursuing what seems promising and abandoning likely dead-ends.
Concluding remarks¶
- Polymath as a community that was crowdsourced at the early stages of Polymath 1.
- Effective collaboration through group-attitudes.
- Active aggregation by central figures that are commonly known to be available.
Questions?¶
In [74]:
#TODO: use sizes as node_size
def make_network(ser, network, edge_list):
edge_dict = {(i,j): k for (i,j,k) in edge_list}
df = DataFrame(index=ser.index, columns=ser.index)
for (i,j) in edge_dict.keys():
df[i][j] = edge_dict[(i,j)]
df = df.fillna(0)
network.add_nodes_from(ser.index)
network.add_weighted_edges_from(edge_list)
return network,df
def digraph_from_series_of_sets(ser):
network = nx.DiGraph()
edge_list = [(i, j, (len(ser[i] & ser[j]) / len(ser[i]))) for (i, j) in permutations(ser.index, 2)]
return make_network(ser, network, edge_list)
def graph_from_series_of_sets(ser):
network = nx.Graph()
edge_list = [(i, j, (len(ser[i] & ser[j]) / len(ser[i] | ser[j]))) for (i, j) in combinations(ser.index, 2)]
#edge_list = [(i, j, (len(ser[i] & ser[j]))) for (i, j) in combinations(ser.index, 2)]
return make_network(ser, network, edge_list)
In [62]:
thread_ser = PM_FRAME['authors'].copy()
thread_ser.index = [project + " " + str(i) for (project, i) in thread_ser.index.values]
a_netw, a_df = graph_from_series_of_sets(thread_ser)
nx.draw_networkx(a_netw)
In [68]:
a_netw = nx.DiGraph([(u,v,d) for u,v,d in a_netw.edges(data=True) if d['weight'] >= .33])
a_netw_edges = a_netw.edges()
a_netw_weights = [a_netw[source][dest]['weight'] for source, dest in a_netw_edges]
a_netw_colors = [plt.cm.Blues(weight*15) for weight in a_netw_weights]
nx.draw_networkx(a_netw, edges=a_netw_edges, width=1, edge_color=a_netw_colors)
In [53]:
for comp in nx.weakly_connected_components(a_netw):
if len(comp) > 1:
for project in sorted(list(comp)):
print(project)
print()
In [86]:
project_ser = get_last(POLYMATHS)[0]['authors (accumulated)']
project_network, project_matrix = graph_from_series_of_sets(project_ser)
project_nodes, project_sizes = project_ser.index.tolist(), project_ser.apply(len).tolist()
project_edges = project_network.edges()
project_weights = [project_network[source][dest]['weight'] for source, dest in project_edges]
project_colors = [plt.cm.Blues(weight*15) for weight in project_weights]
nx.draw_networkx(project_network, nodelist=project_nodes, node_size=project_sizes, edges=project_edges, width=1, edge_color=project_colors)
project_matrix
Out[86]:
In [85]:
project_nodes.tolist()
Out[85]:
In [71]:
f_project_network = nx.Graph([(u,v,d) for u,v,d in project_network.edges(data=True) if d['weight'] >= .075])
f_project_edges = f_project_network.edges()
f_project_weights = [f_project_network[source][dest]['weight'] for source, dest in f_project_edges]
f_project_colors = [plt.cm.Blues(weight*15) for weight in f_project_weights]
nx.draw_networkx(f_project_network, edges=f_project_edges, width=1, edge_color=f_project_colors)
In [34]:
DataFrame.from_records([(1,2,3), (2,1,4)])
Out[34]:
In [ ]: